Vegetation Remote Sensing

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1 Vegetation Remote Sensing Huade Guan Prepared for Remote Sensing class Earth & Environmental Science University of Texas at San Antonio November 2, 2005

2 Outline Why do we study vegetation remote sensing? What characteristics of vegetation (or vegetation cover) do we usually measure? How can we quantify these characteristics from remotely sensed imagery? A case study of quantifying fractional vegetation cover

3 GEOBASE results for: kw: vegetation and (kw: remote w sensing). Records found: 4,593 GEOBASE results for: kw: vegetation and (kw: remote w sensing) and kw: climate. Records found: 322; GEOBASE results for: kw: vegetation and (kw: remote w sensing) and kw: ecology. Records found: 814; GEOBASE results for: kw: vegetation and (kw: remote w sensing) and kw: hydrology. (Records found: 138; GEOBASE results for: kw: vegetation and (kw: remote w sensing) and kw: agriculture. Records found: 238; GEOBASE results for: kw: vegetation and (kw: remote w sensing) and kw: forest. Records found: 792

4 Vegetation effect on climate Global distribution of tropical savannas (Hoffmann & Jackson, 2000)

5 Modeled change from replacing savannas with grassland (Hoffmann & Jackson, 2000)

6 Modeled change from replacing savannas with grassland (Hoffmann & Jackson, 2000)

7 Pictures courtesy of Bradford Wilcox, Texas A&M Ecological change

8 Vegetation and hydrology From Scanlon et al. 2005

9 Ponderosa Juniper Grass Creosote Recharge: Creosote: ~0 mm/yr Grass: < 0.1 Juniper: 0.4 Ponderosa: 2.3 Sandvig, 2005

10 Agriculture Crop types and areas Irrigation Growth regulator application Stress detection Yield estimates

11 Forest An example, remotely sensed fuel moisture content

12 Why do we study vegetation remote sensing? What characteristics of vegetation (or vegetation cover) do we usually measure? How can we quantify these characteristics from remotely sensed imagery? A case study of quantifying fractional vegetation cover

13 What do we measure? Fractional vegetation cover Vegetation type Vegetation health status Stress (water, disease, ) Leaf area index,

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16 Why do we study vegetation remote sensing? What characteristics of vegetation (or vegetation cover) do we usually measure? How can we quantify these characteristics from remotely sensed imagery? A case study of quantifying fractional vegetation cover?

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20 Absorption Efficiency Chlorophyll b Chlorophyll a Absorption Spectra of Chlorophyll a and b Absorption Spectra of Chlorophyll a and b, β-carotene, Pycoerythrin, and Phycocyanin Pigments a violet blue green yellow red Wavelength, µm Phycocyanin Phycoerythrin Chlorophyll a peak absorption is at 0.43 and 0.66 µm. Absorption Efficiency b. β-carotene violet blue green yellow red Wavelength, µm Chlorophyll b peak absorption is at 0.45 and 0.65 µm. Optimum chlorophyll absorption windows are: µm m and µm Jensen, 2000

21 Water absorption bands: 0.97 µm 1.19 µm 1.45 µm 1.94 µm 2.70 µm Jensen, 2000

22 Spectral reflectance changes with foliage water content Jensen, 2000

23 Near infrared spruce soil reflectance Red Wavelength (nm)

24 Vegetation Indices The generic normalized difference vegetation index (NDVI): NDVI= NIR red NIR+ red has provided a method of estimating net primary production over varying biome types (e.g. Lenney et al., 1996), identifying ecoregions (Ramsey et al., 1995), monitoring phenological patterns of the earth s vegetative surface, and of assessing the length of the growing season and dry-down down periods (Huete( and Liu, 1994).

25 Vegetation Indices

26 Vegetation Indices More indices related to vegetation water stress (reviewed by Stimson et al. 2005): 1. Water absorption index (R895/R972) 2. Normalized difference infrared index [(R819-R1649)/(R819+R1649)] 3. Equivalent water thickness (R867~R1049) 4. Red edge (less subject to background effect) spruce soil reflectance Wavelength (nm)

27 Vegetation Indices Derive biophysical variables from vegetation indices For example, 1) Leaf area index: LAI = a + b NDVI 2) Fractional photosynthetically active radiation FPAR = a + b NDVI Both are related to NPP, a measurement of plant growth obtained by calculating the quantity of carbon absorbed and stored by vegetation.

28 Why do we study vegetation remote sensing? What characteristics of vegetation (or vegetation cover) do we usually measure? How can we quantify these characteristics from remotely sensed imagery? A case study of quantifying fractional vegetation cover

29 Study sites 2 1 3

30 Method (1) Pixel reflectance = Vegetation reflectance * Fr + Soil reflectance *(1-Fr) Or (2) Pixel NDVI = Vegetation NDVI * Fr + Soil NDVI *(1-Fr) A pixel

31 Collect End-member Spectral signature

32 For Pinyon ~ Juniper site Results 0.6 For shrub site 0.5 model derived Frr _LRS 2_LN 3_LRS 3_LN Reflectance model gave: Fr = 0.30; NDVI model gave: Fr = Field measured Fr Measured Fr: ~ 0.3 Why?

33 Work undergoing October 19, 2005

34 Exercises (1) Convert radiance measurements to spectral reflectance, and plot the result (2) Find the red-edge inflection point by looking at the first derivative of the reflectance-wavelength curve (3) Calculate NDVI using Landsat ETM bandwidths Red: , NIR: (4) Calculate NDVI using MODIS bandwidths Red: , NIR: (5) How much difference are the numbers from (2) and (3)?

Many of remote sensing techniques are generic in nature and may be applied to a variety of vegetated landscapes, including

Many of remote sensing techniques are generic in nature and may be applied to a variety of vegetated landscapes, including Remote Sensing of Vegetation Many of remote sensing techniques are generic in nature and may be applied to a variety of vegetated landscapes, including 1. Agriculture 2. Forest 3. Rangeland 4. Wetland,

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